Descripción del proyecto
Un nuevo marco para la predicción eficaz del ensamblaje conjunto de péptidos
Desde hace poco, existe un interés creciente por el autoensamblaje supramolecular, en particular por el ensamblaje conjunto de péptidos multicomponentes. Esta tecnología parece muy prometedora para las industrias basadas en materiales, ya que mejora la utilidad funcional de los materiales a base de péptidos. Sin embargo, la comprensión de la complejidad de los materiales basados en péptidos y la predicción de las estructuras de los ensamblajes conjuntos han constituido grandes obstáculos para el avance de esta tecnología. El equipo del proyecto SupraModel, financiado con fondos europeos, tiene como objetivo desarrollar un marco eficaz y rápido para predecir estructuras de ensamblaje conjunto de péptidos y, a su vez, proporcionar información sobre el proceso de ensamblaje conjunto. Con este proyecto se establecerán nuevas normas de diseño para mejorar la eficiencia de futuros proyectos.
Objetivo
Supramolecular self-assembly is a fundamental process abundantly utilized by nature and emerging functional materials technologies ranging from drug delivery to soft semiconductor devices. Recently, an increased focus has been placed on the multicomponent peptide co-assembly as they often display unique emergent properties that can dramatically expand the functional utility of peptide-based materials. Still, the full potential is hindered by the combinatorial complexity of peptide-based materials and our inability to predict the co-assembled structures and, therefore, properties and functionality. Machine Learning models built on top of Molecular Dynamics simulations are ideally suited to decipher the co-assembly behavior. However, the existing molecular models either suffer from severe approximations disabling them to give accurate predictions or are computationally too expensive to transverse the material space. Addressing this trade-off, I aim to develop a computational framework for fast and accurate peptide co-assembly prediction using as a key strategy a multiscale construction of Graph Neural Network-based models that can predict the peptide co-assembly. This innovative approach will enable me to reach the following objectives: (1) obtain unprecedented molecular insight into the peptide co-assembly process inaccessible to experiments, (2) uncover novel candidate materials, and (3) provide rational design rules for multicomponent peptide-based supramolecular materials. In a broader context, increased insight into cooperative behavior will bring us closer to understanding and ultimately synthetically replicating the exceptional functionality of living systems, while the methodological advancements of data-driven molecular modeling will be of paramount importance in other areas of biomaterial engineering and beyond.
Ámbito científico
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
80333 Muenchen
Alemania